Distributed Gradient Descent Algorithm Robust to an Arbitrary Number of Byzantine Attackers

Due to the growth of modern dataset size and the desire to harness computing power of multiple machines, there is a recent surge of interest in the design of distributed machine learning algorithms. However, distributed algorithms are sensitive to Byzantine attackers who can send falsified data to p...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 67; H. 22; S. 5850 - 5864
Hauptverfasser:	Cao, Xinyang, Lai, Lifeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 15.11.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Byzantine attacker Convergence Datasets distributed gradient descent Ground truth Machine learning Optimization Radio frequency Robustness (mathematics) Servers Signal processing algorithms Sociology Statistics
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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